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Record W3185501639 · doi:10.1097/phm.0000000000001801

Ultrasound With E-Stimulation Diagnostic Nerve Blocks for Targeted Muscle Selection in Spasticity

2021· article· en· W3185501639 on OpenAlexaff
Paul Winston, Mahdis Hashemi, Daniel Vincent

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2021
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineSpasticitySpasticNerve blockNeurolysisBotulinum toxinRange of motionNeurectomyPhysical medicine and rehabilitationAnatomyAnesthesiaSurgeryPathology

Abstract

fetched live from OpenAlex

The diagnostic nerve block has been used to assist in the evaluation of a muscle’s contribution to spasticity for decades.1 The diagnostic nerve block is helpful to evaluate the range of motion to determine whether a true muscle contracture is present as opposed to a reducible deformity and to evaluate the strength of antagonist muscles when the muscle is paralyzed and range of motion increases because of a cessation of the spastic muscle overactivity.2 The French Clinical Guidelines, published in 2019, describe the techniques in detail and note that there is insufficient evidence for the superiority of ultrasound (US) over e-stimulation with surface localization alone.1 Ultrasound allows for direct nerve and vascular identification. The combination of US and e-stimulation aids in localization of a targeted nerve with a reduced volume required of phenol.3 The complex but readily identifiable fascicular branching of nerves and their relationship to the neurovascular bundle is demonstrated in a detailed study of the branches of the tibial nerve.2 The diagnostic nerve block offers much information to the clinician. It may demonstrate which muscle is the main contributor to a spastic overactive pattern, such as differentiating the elbow flexor muscles or the muscles responsible for the spastic equinovarus foot.2,3 After identifying a targeted muscle that is most responsible for the spastic overactive positioning, it can aid in guiding therapy with botulinum toxin, phenol and alcohol, surgical neurectomy, or a mini-invasive percutaneous neurotomy. The addition of US allows for the direct visualization of blood vessels, thus reducing the chance of injury.2 The key vascular structures importantly serve as a beacon for landmarking. As a color Doppler feature is ubiquitous on most US machines, the localization is easily confirmed in real time. Not only does color Doppler reduce chance of puncture, the well-known mantra of vein-artery-nerve serves to identify that key nerve branches will be located as they course around the vascular structures. We have provided a video vignette that demonstrates how this intimate relationship of nerves and their fascicles to vascular structures allows for rapid recognition of the targeted nerve upon ultrasound scanning. We demonstrate some of the most common nerves targeted in spasticity management: obturator, femoral, lateral pectoral, musculocutaneous, and the tibial nerve fascicular branches. Our technique illustrates how the color feature allows for localization of the guiding blood vessels, which can be seen to pulsate even without color. The US also allows for demonstration that the needle can be placed at the desired nerve without the trial-and-error placement and replacement of e-stimulation alone. Furthermore, US visualization ensures that only the desired nerve branch to a targeted muscle is stimulated. Finally, we demonstrate the use of a cryoneurotomy probe for targeted localization of the tibial nerve branches to the gastrocnemius and tibialis posterior muscles, which requires sustained contact to the nerve.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.272
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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